• ETRI Journal
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• v.32 no.5
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• pp.687-694
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• 2010

Breaks in presence (BIP) are those moments during virtual environment (VE) exposure in which participants become aware of their real world setting and their sense of presence in the VE becomes disrupted. In this study, we investigate participants' experience when they encounter technical anomalies during game play. We induced four technical anomalies and compared the BIP responses of a navigation mode game to that of a combat mode game. In our analysis, we applied a linear mixed model (LMM) and compared the results with those of a conventional regression model. Results indicate that participants felt varied levels of impact and recovery when experiencing the various technical anomalies. The impact of BIPs was clearly affected by the game mode, whereas recovery appears to be independent of game mode. The results obtained using the LMM did not differ significantly from those obtained using the general regression model; however, it was shown that treatment effects could be improved by consideration of random effects in the regression model.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.1037-1047
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• 2012

The generalized linear mixed model(GLMM) is widely used in fitting categorical responses of clustered data. In the numerical approximation of likelihood function the normality is assumed for the random effects distribution; subsequently, the commercial statistical packages also routinely fit GLMM under this normality assumption. We may also encounter departures from the distributional assumption on the response variable. It would be interesting to investigate the impact on the estimates of parameters under misspecification of distributions; however, there has been limited researche on these topics. We study the sensitivity or robustness of the maximum likelihood estimators(MLEs) of GLMM for counts data when the true underlying distribution is normal, gamma, exponential, and a mixture of two normal distributions. We also consider the effects on the MLEs when we fit Poisson-normal GLMM whereas the outcomes are generated from the negative binomial distribution with overdispersion. Through a small scale Monte Carlo study we check the empirical coverage probabilities of parameters and biases of MLEs of GLMM.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.445-452
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• 2009

Credibility models are actuarial tools to distribute premiums fairly among a heterogeneous group of policyholders. Many existing credibility models can be expressed as special cases of linear mixed effects models. In this paper we propose a nonlinear credibility regression model by reforming the linear mixed effects model through kernel machine. The proposed model can be seen as prediction method applicable in any setting where repeated measures are made for subjects with different risk levels. Experimental results are then presented which indicate the performance of the proposed estimating procedure.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.625-637
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• 2015

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

• Child Studies in Asia-Pacific Contexts
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• v.5 no.1
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• pp.51-62
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• 2015

This study examined the role of ethnicity and extended family relationship in the longitudinal patterns of unmarried resident fathers' engagement with young children in the U.S. Using three waves of panel data from the Fragile Families and Child Wellbeing Study (FFCWS), data obtained from 292 respondents were analyzed by the Linear Mixed Effects Regression (LMER) method. Findings suggested that Black unmarried resident fathers exhibited more engagement with their infants at age one than their White counterparts did, but the initial difference reversed by age five as a result of decreasing engagement among Black fathers over time and comparatively increasing engagement among Whites. Results also suggested that Black unmarried resident fathers with a high relationship level with their extended family members exhibited less engagement with their infants than Black fathers with a low relationship level, but the initial difference reversed by age five as a result of decreasing engagement among Black fathers with a low relationship level over time and comparatively increasing engagement among Black fathers with a high relationship level.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.957-981
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• 2017

A generalized linear mixed model is an extension of a generalized linear model that allows random effect as well as provides flexibility in developing a suitable model when observations are correlated or when there are other underlying phenomena that contribute to resulting variability. We describe maximum likelihood estimation methods for logistic regression models that include random effects - the Laplace approximation, Gauss-Hermite quadrature, adaptive Gauss-Hermite quadrature, and pseudo-likelihood. Applications are provided with social science problems by analyzing the effect of mental health and life satisfaction on volunteer activities from Korean welfare panel data; in addition, we observe that the inclusion of random effects in the model leads to improved analyses with more reasonable inferences.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.941-949
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• 2014

Unit level logistic regression model with mixed effects has been used for estimating small area proportions, which treats the spatial effects as random effects and assumes linearity between the logistic link and the covariates. However, when the functional form of the relationship between the logistic link and the covariates is not linear, it may lead to biased estimators of the small area proportions. In this paper, we relax the linearity assumption and propose two types of kernel-based logistic regression models for estimating small area proportions. We also demonstrate the efficiency of our propose models using simulated data and real data.

• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.423-433
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• 2014

Test statistics using cumulative sums of residuals have been widely used in various regression models including generalized linear models(GLM). Recently, Pan and Lin (2005) extended this testing procedure to the generalized linear mixed models(GLMM) having random effects, in which we encounter difficulties in computing the marginal likelihood that is expressed as an integral of random effects distribution. The Gaussian quadrature algorithm is commonly used to approximate the marginal likelihood. Many commercial statistical packages provide an option to apply this type of goodness-of-fit test in GLMs but available programs are very rare for GLMMs. We suggest a computational algorithm to implement the testing procedure in GLMMs by a freely accessible R package, and also illustrate through practical examples.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.269-279
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• 2015

This paper considers inferences of random effects. We show that the proposed confidence distribution (CD) performs well in logistic regression for random intercepts with small samples. Real data analyses are also done to identify the subject effects clearly.

• Journal of Periodontal and Implant Science
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• v.45 no.1
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• pp.2-7
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• 2015

A fundamental problem in analyzing complex multilevel-structured periodontal data is the violation of independency among the observations, which is an assumption in traditional statistical models (e.g., analysis of variance and ordinary least squares regression). In many cases, aggregation (i.e., mean or sum scores) has been employed to overcome this problem. However, the aggregation approach still exhibits certain limitations, such as a loss of power and detailed information, no cross-level relationship analysis, and the potential for creating an ecological fallacy. In order to handle multilevel-structured data appropriately, mixed effects models have been introduced and employed in dental research using periodontal data. The use of mixed effects models might account for the potential bias due to the violation of the independency assumption as well as provide accurate estimates.